Data Loading

url_in <- "https://data.cityofnewyork.us/api/views/833y-fsy8/rows.csv?accessType=DOWNLOAD"
shootings_raw <- read_csv(url_in)
Rows: 29744 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (12): OCCUR_DATE, BORO, LOC_OF_OCCUR_DESC, LOC_CLASSFCTN_DESC, LOCATION...
dbl   (5): INCIDENT_KEY, PRECINCT, JURISDICTION_CODE, Latitude, Longitude
num   (2): X_COORD_CD, Y_COORD_CD
lgl   (1): STATISTICAL_MURDER_FLAG
time  (1): OCCUR_TIME

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(shootings_raw)

A tibble: 6 × 21

INCIDENT_KEY OCCUR_DATE OCCUR_TIME BORO LOC_OF_OCCUR_DESC PRECINCT

Initial Look

summary(shootings_raw)

INCIDENT_KEY OCCUR_DATE OCCUR_TIME BORO
Min. : 9953245 Length:29744 Length:29744 Length:29744
1st Qu.: 67321140 Class :character Class1:hms Class :character
Median :109291972 Mode :character Class2:difftime Mode :character
Mean :133850951 Mode :numeric
3rd Qu.:214741917
Max. :299462478

LOC_OF_OCCUR_DESC PRECINCT JURISDICTION_CODE LOC_CLASSFCTN_DESC Length:29744 Min. : 1.00 Min. :0.0000 Length:29744
Class :character 1st Qu.: 44.00 1st Qu.:0.0000 Class :character
Mode :character Median : 67.00 Median :0.0000 Mode :character
Mean : 65.23 Mean :0.3181
3rd Qu.: 81.00 3rd Qu.:0.0000
Max. :123.00 Max. :2.0000
NA’s :2
LOCATION_DESC STATISTICAL_MURDER_FLAG PERP_AGE_GROUP
Length:29744 Mode :logical Length:29744
Class :character FALSE:23979 Class :character
Mode :character TRUE :5765 Mode :character

PERP_SEX PERP_RACE VIC_AGE_GROUP VIC_SEX
Length:29744 Length:29744 Length:29744 Length:29744
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character

VIC_RACE X_COORD_CD Y_COORD_CD Latitude
Length:29744 Min. : 914928 Min. :125757 Min. :40.51
Class :character 1st Qu.:1000094 1st Qu.:183042 1st Qu.:40.67
Mode :character Median :1007826 Median :195506 Median :40.70
Mean :1009442 Mean :208722 Mean :40.74
3rd Qu.:1016739 3rd Qu.:239980 3rd Qu.:40.83
Max. :1066815 Max. :271128 Max. :40.91
NA’s :97
Longitude Lon_Lat
Min. :-74.25 Length:29744
1st Qu.:-73.94 Class :character
Median :-73.91 Mode :character
Mean :-73.91
3rd Qu.:-73.88
Max. :-73.70
NA’s :97

glimpse(shootings_raw)

Rows: 29,744 Columns: 21 $ INCIDENT_KEY 231974218, 177934247, 255028563, 25384540, 726… $ OCCUR_DATE “08/09/2021”, “04/07/2018”, “12/02/2022”, “11/… $ OCCUR_TIME 

colnames(shootings_raw)

[1] “INCIDENT_KEY” “OCCUR_DATE”
[3] “OCCUR_TIME” “BORO”
[5] “LOC_OF_OCCUR_DESC” “PRECINCT”
[7] “JURISDICTION_CODE” “LOC_CLASSFCTN_DESC”
[9] “LOCATION_DESC” “STATISTICAL_MURDER_FLAG” [11] “PERP_AGE_GROUP” “PERP_SEX”
[13] “PERP_RACE” “VIC_AGE_GROUP”
[15] “VIC_SEX” “VIC_RACE”
[17] “X_COORD_CD” “Y_COORD_CD”
[19] “Latitude” “Longitude”
[21] “Lon_Lat”

Cleaned Data

shootings_clean <- shootings_raw %>%
  mutate(
    OCCUR_DATE = mdy(OCCUR_DATE),
    BORO = as.factor(BORO),
    PERP_SEX = as.factor(PERP_SEX),
    PERP_RACE = as.factor(PERP_RACE),
    VIC_SEX = as.factor(VIC_SEX),
    VIC_RACE = as.factor(VIC_RACE)
  ) %>%
  mutate(
    Period = case_when(
      OCCUR_DATE >= as.Date("2006-01-01") & OCCUR_DATE <= as.Date("2010-12-31") ~ "2006–2010",
      OCCUR_DATE >= as.Date("2011-01-01") & OCCUR_DATE <= as.Date("2015-12-31") ~ "2011–2015",
      OCCUR_DATE >= as.Date("2016-01-01") & OCCUR_DATE <= as.Date("2020-12-31") ~ "2016–2020",
      OCCUR_DATE >= as.Date("2021-01-01") ~ "2021–2024",
      TRUE ~ NA_character_
    )
  ) %>%
  rename(
    `Occur Date` = OCCUR_DATE,
    `Perp Sex` = PERP_SEX,
    `Perp Race` = PERP_RACE,
    `Perp Age Group` = PERP_AGE_GROUP,
    `Victim Sex` = VIC_SEX,
    `Victim Race` = VIC_RACE,
    `Victim Age Group` = VIC_AGE_GROUP
  ) %>%
  select(
    -INCIDENT_KEY,
    -OCCUR_TIME,
    -LOC_OF_OCCUR_DESC,
    -PRECINCT,
    -JURISDICTION_CODE,
    -LOC_CLASSFCTN_DESC,
    -LOCATION_DESC,
    -STATISTICAL_MURDER_FLAG,
    -X_COORD_CD,
    -Y_COORD_CD,
    -Lon_Lat
  )

Occur Date BORO Perp Age Group Perp Sex
Min. :2006-01-01 BRONX : 8834 Length:29744 (null): 1628
1st Qu.:2009-10-29 BROOKLYN :11685 Class :character F : 461
Median :2014-03-25 MANHATTAN : 3977 Mode :character M :16845
Mean :2014-10-31 QUEENS : 4426 U : 1500
3rd Qu.:2020-06-29 STATEN ISLAND: 822 NA’s : 9310
Max. :2024-12-31

      Perp Race     Victim Age Group   Victim Sex

BLACK :12323 Length:29744 F: 2891
WHITE HISPANIC: 2667 Class :character M:26841
UNKNOWN : 1838 Mode :character U: 12
(null) : 1628
BLACK HISPANIC: 1487
(Other) : 491
NA’s : 9310
Victim Race Latitude Longitude
AMERICAN INDIAN/ALASKAN NATIVE: 13 Min. :40.51 Min. :-74.25
ASIAN / PACIFIC ISLANDER : 478 1st Qu.:40.67 1st Qu.:-73.94
BLACK :20999 Median :40.70 Median :-73.91
BLACK HISPANIC : 2930 Mean :40.74 Mean :-73.91
UNKNOWN : 72 3rd Qu.:40.83 3rd Qu.:-73.88
WHITE : 741 Max. :40.91 Max. :-73.70
WHITE HISPANIC : 4511 NA’s :97 NA’s :97
Period
Length:29744
Class :character
Mode :character

Rows: 29,744 Columns: 11 $ Occur Date 2021-08-09, 2018-04-07, 2022-12-02, 2006-11-19, 20… $ BORO BRONX, BROOKLYN, BRONX, BROOKLYN, BRONX, BRONX, BRO… $ Perp Age Group NA, “25-44”, “(null)”, “UNKNOWN”, “25-44”, “18-24”,… $ Perp Sex NA, M, (null), U, M, M, NA, NA, M, M, M, M, M, M, N… $ Perp Race NA, WHITE HISPANIC, (null), UNKNOWN, BLACK, BLACK, … $ Victim Age Group “18-24”, “25-44”, “25-44”, “18-24”, “<18”, “18-24”,… $ Victim Sex M, M, M, M, F, M, M, M, M, M, M, M, M, M, M, M, M, … $ Victim Race BLACK, BLACK, BLACK, BLACK, BLACK, BLACK, BLACK, WH… $ Latitude 40.80967, 40.68561, 40.87235, 40.64249, 40.84598, 4… $ Longitude -73.92019, -73.94291, -73.86823, -73.99691, -73.907… $ Period “2021–2024”, “2016–2020”, “2021–2024”, “2006–2010”,… [1] “Occur Date” “BORO” “Perp Age Group” “Perp Sex”
[5] “Perp Race” “Victim Age Group” “Victim Sex” “Victim Race”
[9] “Latitude” “Longitude” “Period”

Interactive Map

# filtering missing coordinates
shooting_coords <- shootings_raw %>%
  filter(!is.na(Longitude), !is.na(Latitude))

# Creating interactive map
leaflet(shooting_coords) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(
    lng = ~Longitude,
    lat = ~Latitude,
    radius = 2,
    color = "red",
    stroke = FALSE,
    fillOpacity = 0.2,
    popup = ~paste0(
      "Date: ", OCCUR_DATE,
      "<br>Borough: ", BORO,
      "<br>Victim Sex: ", VIC_SEX,
      "<br>Perpetrator Sex: ", PERP_SEX
    )
  ) %>%
  addLegend("bottomright", colors = "red", labels = "Shooting Incident")
NYC Borough Population by Decade
Year Borough Population
2000 Manhattan 1537195
2000 Brooklyn 2465326
2000 Queens 2229379
2000 Bronx 1332650
2000 Staten Island 443728
2010 Manhattan 1585873
2010 Brooklyn 2504700
2010 Queens 2230722
2010 Bronx 1385108
2010 Staten Island 468730
2020 Manhattan 1694251
2020 Brooklyn 2736074
2020 Queens 2405464
2020 Bronx 1472654
2020 Staten Island 495747

Statistical Analysis

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

NYC Borough Population Share by Year (%)
Year Manhattan Brooklyn Queens Bronx Staten Island
2000 19.2 30.8 27.8 16.6 5.5
2010 19.4 30.6 27.3 16.9 5.7
2020 19.2 31.1 27.3 16.7 5.6 
shootings_clean %>%
  filter(!is.na(BORO), BORO != "") %>%
  count(BORO) %>%
  ggplot(aes(x = reorder(BORO, n), y = n, fill = BORO)) +
    geom_col(show.legend = FALSE) +
    labs(
      title = "Number of Shooting Incidents by Borough",
      x     = "Borough",
      y     = "Total Shootings"
    ) +
    theme_minimal(base_size = 14) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

Percentage of Shooting Incidents by Borough
Borough Number of Shootings Percent of Total (%)
BROOKLYN 11685 39.3
BRONX 8834 29.7
QUEENS 4426 14.9
MANHATTAN 3977 13.4
STATEN ISLAND 822 2.8

Chi-Square Goodness-of-Fit Test

Observed vs. Expected Counts
Borough Observed Expected
Bronx 8834 4988
Brooklyn 11685 9173
Manhattan 3977 5734
Queens 4426 8172
Staten Island 822 1676 
Chi-squared test for given probabilities

data: shootings_obs$Observed X-squared = 6344.6, df = 4, p-value < 2.2e-16 ## Observed vs. Expected Counts by Borough

Observed vs. Expected Counts by Borough (Grouped Bars)

Conclusion

I used a chi-square goodness-of-fit test to compare the proportions of shootings in each borough to their proportion of population to all of NYC. The reasoning behind choosing this test is because I was seeking to compare five discrete observed categories (the different boroughs) to an expected distribution. The expected distribution being that the proportion of shootings in NYC for each borough would be similiar to the population proportion. From the basic analysis and charts above, it was easy to tell that was not the case. The most populated borough, Brooklyn, had a MPP (mean population proportion) of about 30.83% across the past three decennial censuses had a BSP (borough shooting proportion) of 39.3%. The observed proportion is much lower than the expected. While Staten Island had a MPP of 5.6% and a BSP of 2.8%. Staten Island experienced half the expected shootings for its population.

The test yielded a chi-square value of 6344.6 with 4 degress of a freedom and a p-value way below the threshold (p<0.00001). I reject the null hypothesis. The BSP’s are not inline with the MPP’s.

I spent years of as a research assistant in a social psychology lab at a university. I found that research incredibly engaging because it helped the world make sense to me. That there existed all of these hidden phenomenoms in the world. The pattern of shootings is not just random and there are reasons for the shootings. My assumption is that if I did further analysis into this topic, I would find patterns of shooting that coincide with lower socioeconomic status or maybe certain streets that have more bars are prone to having more violent incidences or maybe some areas have a lack of police prescence compared to others. I do not have the answers and there will never be any exact answers. We can only hope to discern the patterns of society.

Bias Identification

Often, we associate the word “bias” with inner-prejudiced thoughts or opinions. Or maybe its favoring of one thing over another. Bias is different in research. Bias for this data is something that you have to look beyond the numbers. One instance of bias could be under-reporting of shooting incidents in one borough or neighborhood over another. Maybe one is more prone to violent incidents so there is a loss of sensitivity to shootings and “minor shootings” go unreported. There could be mistakes in reporting the location of a shooting. Maybe the streets get written down wrong. The last bias I’ll mention is one within the researcher. There is data regarding ethnicity of either the perpetrator or the victim. If there was someone who had racial prejudice against a certain group, they might find themselves trying to change the analysis to fit their hateful thoughts.

I think the greatest way to reduce bias is simply being aware of it. If you know of its existence then you will not wander into it. Bias can be further eliminated with more and more research that compounds on your findings. If your data says one thing and 20 other people have data saying the same thing, then that adds validity to your research and is an indication of low bias.